When　bearings,　a　key　component　of　rotating　machinery,　fail　during　the　operation　of　the　equipment,　the　failure　features　are　relatively　weak.　Without　timely　monitoring　and　diagnosis,　it　is　easy　to　cause　downtime　and　even　major　safety　accidents.　Therefore,　an　algorithm　based　on　center　frequency　adaptive　filter　assisted　sparse　(CFAFAS)　is　proposed,　which　can　be　used　to　extract　the　fault　features　in　the　signal.　First,　a　center　frequency-based　unilateral　attenuation　filter　bank　is　constructed　by　unilateral　attenuation　wavelets.　The　filter　atoms　matching　the　signal　are　obtained　by　adaptive　selection　of　the　filter　bank.　Then,　the　obtained　filter　atoms　are　used　for　convolutional　sparse　coding　to　reduce　the　redundant　components　of　the　signal,　and　the　sparse　coefficients　characterizing　the　impact　components　of　the　signal　are　obtained.　Finally,　the　sparse　coefficients　are　analyzed　by　envelope　spectrum　to　determine　the　fault　types.　Simulation　experiments　with　test　bench-bearing　fault　signals　verify　the　effectiveness　of　the　CFAFAS　algorithm.　Meanwhile,　through　the　comparison　experiment　of　the　traditional　fast　spectral　kurtosis　method　and　GMC　sparse　enhancement　diagnostic　method,　the　comparison　method　cannot　clearly　extract　the　fault　feature　frequency,　which　verifies　that　the　CFAFAS　algorithm　has　a　better　feature　extraction　effect.　©　2023　IEEE.